LEADER 04961nam  22006495  450 
001 9910337885603321
005 20200701115253.0
010   $a3-319-93897-5
024 7 $a10.1007/978-3-319-93897-4
035   $a(CKB)4100000006374706
035   $a(MiAaPQ)EBC5509321
035   $a(DE-He213)978-3-319-93897-4
035   $a(PPN)230541925
035   $a(EXLCZ)994100000006374706
100   $a20180903d2019      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aStatistical Approaches for Landslide Susceptibility Assessment and Prediction /$fby Sujit Mandal, Subrata Mondal
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (200 pages)
311   $a3-319-93896-7 
320   $aIncludes bibliographical references and index.
327   $aConcept on Landslides and Landslide Susceptibility -- Geomorphic, Geo-tectonic and Hydrologic Attributes and Landslide Probability -- Frequency Ratio Model (FRM) and Information Value Model (IVM) in landslide susceptibility Assessment and Prediction -- Logistic Regression Model (LRM) and Landslide Susceptibility: A RS & GIS based approach -- Artificial Neural Network (ANN) Model and Landslide Susceptibility -- Weighted Index Overlay Model (WIOM), Certainty factor approach (CFA) and Analytical Hierarchy Process (AHP) in Landslide Susceptibility Studies -- Knowledge driven Statistical Approach for Landslide Susceptibility Assessment using GIS and Fuzzy Logic -- Comparison between Statistical Models: A Review and Evaluation -- Index.
330   $aThis book focuses on the spatial distribution of landslide hazards of the Darjeeling Himalayas. Knowledge driven methods and statistical techniques such as frequency ratio model (FRM), information value model (IVM), logistic regression model (LRM), index overlay model (IOM), certainty factor model (CFM), analytical hierarchy process (AHP), artificial neural network model (ANN), and fuzzy logic have been adopted to identify landslide susceptibility. In addition, a comparison between various statistical models were made using success rate cure (SRC) and it was found that artificial neural network model (ANN), certainty factor model (CFM) and frequency ratio based fuzzy logic approach are the most reliable statistical techniques in the assessment and prediction of landslide susceptibility in the Darjeeling Himalayas. The study identified very high, high, moderate, low and very low landslide susceptibility locations to take site-specific management options as well as to ensure developmental activities in theDarjeeling Himalayas. Particular attention is given to the assessment of various geomorphic, geotectonic and geohydrologic attributes that help to understand the role of different factors and corresponding classes in landslides, to apply different models, and to monitor and predict landslides. The use of various statistical and physical models to estimate landslide susceptibility is also discussed. The causes, mechanisms and types of landslides and their destructive character are elaborated in the book. Researchers interested in applying statistical tools for hazard zonation purposes will find the book appealing.
606   $aNatural disasters
606   $aStatistics 
606   $aPhysical geography
606   $aHydrogeology
606   $aGeoecology
606   $aEnvironmental geology
606   $aNatural Hazards$3https://scigraph.springernature.com/ontologies/product-market-codes/G32000
606   $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020
606   $aPhysical Geography$3https://scigraph.springernature.com/ontologies/product-market-codes/J16000
606   $aHydrogeology$3https://scigraph.springernature.com/ontologies/product-market-codes/G19005
606   $aGeoecology/Natural Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/U21006
615  0$aNatural disasters.
615  0$aStatistics .
615  0$aPhysical geography.
615  0$aHydrogeology.
615  0$aGeoecology.
615  0$aEnvironmental geology.
615 14$aNatural Hazards.
615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
615 24$aPhysical Geography.
615 24$aHydrogeology.
615 24$aGeoecology/Natural Processes.
676   $a363.34
700   $aMandal$b Sujit$4aut$4http://id.loc.gov/vocabulary/relators/aut$0929728
702   $aMondal$b Subrata$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910337885603321
996   $aStatistical Approaches for Landslide Susceptibility Assessment and Prediction$92232938
997   $aUNINA